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Sampling Observations Analytics: Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Sampling Observations Analytics: Record Quality Audit + String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs three local analytical reports on sampling observation records: a record quality audit, string normalisation, and a trailing mean series computation.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied sampling observations records (sample_value, sample_weight). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports: (1) a record quality audit flagging missing, invalid, or anomalous entries across up to 200 rows; (2) a string normalisation report cleaning and standardising text fields like row_id and dataset_group; and (3) a trailing mean series computed over the specified numeric field (sample_value or sample_weight) using the specified rolling window size.
Example use cases
Pre-analysis data quality check
“I have 120 rows of sampling observations with sample_value and sample_weight fields across two dataset groups. Can you run a full record quality audit and string normalisation on them, and also compute a trailing mean series with window size 5 on the sample_value field?”
Rolling average trend detection
“I want to see a trailing mean series with a window of 7 for the sample_weight field across my 80 sampling records dated from 2026-01-01 through 2026-03-22 — also run the quality audit and string normalisation reports at the same time.”
Batch sampling record validation
“Before I submit my sampling dataset to the research team, can you audit all 200 rows for record quality issues, normalise any string fields, and compute a trailing mean on sample_value with a window of 10 so I can spot any smoothed trends?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 sampling observation records (with sample_value and sample_weight) and need all three of: a data quality audit, string normalisation, and a trailing mean time series — bundled for $0.01 USDC. Ideal for pre-processing pipelines before statistical analysis, where catching data issues and computing rolling averages in a single call saves cost and round-trips. Not suitable for live data lookups, predictions, or datasets exceeding 200 rows.
Side effects
- Fewer than 1 or more than 200 rows submitted — rejected with validation error
- Invalid date format (not YYYY-MM-DD) causes row rejection
- sample_value or sample_weight outside ±1,000,000,000,000 range causes validation failure
- Invalid field enum value (not 'sample_value' or 'sample_weight') returns error
- Window size less than 2 or greater than 200 causes parameter rejection
- Malformed JSON body returns 400-level error
- Payment not provided or insufficient USDC causes 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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